期刊
出版社
IEEE COMPUTER SOC
DOI: 10.1109/ICFHR2020.2020.00059
关键词
offline signature verification; deep metric learning; point-to-set distance
资金
- NSFC [61936003]
- GD-NSF [2017A030312006]
- National Key Research and Development Program of China [2016YFB1001405]
- Guangdong Intellectual Property Office [2018-10-1]
- Fundamental Research Funds for the Central Universities [x2dxD2190570]
Offline signature verification is a challenging task, where the scarcity of the signature data per writer makes it a fewshot problem. We found that previous deep metric learning based methods, whether in pairs or triplets, are unaware of intra-writer variations and have low training efficiency because only point-to-point (P2P) distances are considered. To address this issue, we present a novel point-to-set (P2S) metric for offline signature verification in this paper. By dividing a training batch into a support set and a query set, our optimization goal is to pull each query to its belonging support set. To further strengthen the P2S metric, a hard mining scheme and a margin strategy are introduced. Experiments conducted on three datasets show the effectiveness of our proposed method.
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